Progressive secret image sharing (PSS) can progressively recover different resolution images or different parts of an image. To protect image privacy on the cloud and IoT application scenarios, this paper first presents an efficient PSS scheme through the integration of two deep learning networks and reversible data hiding in encrypted images. After sharing different parts of the original image, the receiver can progressively recover: a low-resolution image, a mask image where the most sensitive area is peeled off, and the original image according to the number of holding shares and image encryption keys. Some additional data such as signatures, fingerprints, and timestamps can be also embedded into each share on the cloud server, as the data extraction is separate from image recovery. Experimental results demonstrate that the proposed scheme can uniquely and efficiently obtain a recovered image progressively. Our scheme also has a higher security level, and the average embedding rate achieves almost a 1 bpp improvement compared to state-of-the-art schemes on a larger-scale image dataset.

Efficient power management in smart grid relies on collecting fine-grained power consumption data from users. However, these data may reveal sensitive information about individuals' habits and lifestyles. Various multidimensional data aggregation schemes leveraging public key encryption (PKE) algorithms have been proposed to address this problem. Never theless, most of these schemes come with significant performance costs. In addition, if the secret key of a smart meter was leaked, the confidentiality of encrypted user power data could be at risk. In this article, we propose a lightweight, multidimensional, and privacy-preserving data aggregation scheme with fault-tolerance and key-leakage resilience for smart grid without relying on a trusted third party (TTP), named FKLM-PDA, in which a novel data packaging method that transforms users' multidimensional data into a one-dimensional format is designed, enabling data center parse aggregated results in each dimension, reducing computation and communication costs. For better efficiency, an effective encryption algorithm is proposed to replace the expensive additive homomorphic PKE, like the Paillier cryptosystem, which combines a random masking with secret-sharing based key separation, ensuring threshold key-leakage resilience under a bounded, non-colluding leakage model. Furthermore, not only does FKLM-PDA enhance the fault tolerance mechanism of data transmission from smart meters to a corresponding fog node, but also it supports dynamic user management for joining and exiting improving scalability. Security analysis confirms that FKLM-PDA is privacy-preserving and secure while guaranteeing key-leakage resilience, fault tolerance, authentication, and data integrity. Through performance evaluations, FKLM-PDA outper forms the existing schemes and is superior in computation and functional in communication.

The proliferation of video applications has exacerbated digital piracy issues, notably evidenced by unauthorized video digital editing and camcording processes. While existing research has introduced robust watermarking methods to safeguard video copyrights, these methods often address specific attack scenarios, limiting their overall efficacy. To address this problem, we propose a robust blind video watermarking scheme based on Frequency-Spherical Cavity Transformation (FSCT), offering a comprehensive solution for both digital editing and camcording processes. Our approach treats the spatial and temporal aspects of the video as a 3D cube, utilizing FSCT to ensure temporal translational invariance and resilience against spatial attacks. To mitigate artifacts induced by motion characteristics, we analyze the properties of FSCT and introduce a visual quality optimization strategy, enhancing imperceptibility while ensuring robustness. Simultaneously, during extraction process, the watermark can be successfully retrieved from video camcording with only a specified time interval, eliminating the need for temporal synchronization. Through extensive experimentation, the proposed method exhibits superior robustness against digital editing and camcording compared to existing methods.

In watermarking schemes, deriving the geometric invariants of the multimedia content is crucial for modern watermarking against geometric deformations. The invariants based on orthogonal moments can effectively describe the semantic content of multimedia due to their excellent mathematical properties. Modulating watermarked signals into these invariants can yield satisfactory geometric robustness. However, with the emerging risks posed by generative large models, the current theoretical analysis of the relationship between invariants and watermarking is still limited, and the intrinsic connection between them is neglected to varying degrees when designing moments-based watermarking schemes. To bridge this gap, we propose a set of design principles, including the texture complexity priority principle, uniform zeros distribution priority principle, and invariants conjugation priority principle, and reveal the critical influence of the mathematical properties of moments on video watermarking. Based on the above principles, we also propose a texture-aware adaptive video watermarking scheme based on orthogonal moments. Extensive experiments show that the proposed video watermarking scheme outperforms state-of-the-art watermarking algorithms regarding imperceptibility, robustness, and computational complexity.

With the rapid development of mobile Internet and video applications, robust video watermarking technology has become a focal area of research for protecting and tracking intellectual property rights in digital media. An important characteristic of video is that it has both spatial and temporal properties. Previous studies in video watermarking have primarily focused on either spatial or temporal distortions and did not uniformly consider all types of video distortion, which restricts the robustness of video watermarking. In this paper, a novel robust blind video watermarking is proposed by exploring consistent spatio-temporal distortion and stable 3-D DCT coefficients. Our method achieves stronger robustness by uniformly treating the spatial and temporal distortions of the video. The properties of the stable 3-D DCT coefficients are mathematically proved, which makes the scheme generalizable. Extensive experiments have demonstrated that our method is resistant to video compression (H.264/AVC, H.265/HEVC) attacks, geometric attacks, and temporal domain attacks, and outperforms the current state-of-the-art video watermarking schemes.

Existing video watermarking embeds robust watermarks in each frame of the video for copyright protection and tracking. However, just as any content written on a blank paper is easily perceived, embedding watermarks in the texture-poor frames impairs imperceptibility. Common geometric attacks such as scaling and rotation pose a significant challenge to the existing video watermarking. Image watermarking based on moments is robust against geometric attacks. However, moment-based watermarking is difficult to migrate to the video due to its lack of perceptual guarantee and high computational cost. In this paper, we propose an adaptive video watermarking scheme by exploring the relationship between moments and video textures, which can adaptively select texture-rich frames to embed watermarks for perceptual guarantee. Furthermore, we utilize the properties of moment calculation in videos to optimize efficiency. Extensive experiments show that the proposed method can achieve better imperceptibility than existing methods while maintaining strong robustness.

Abstract Blind image watermarking is regarded as a vital technology to provide copyright of digital images. Due to the rapid growth of deep neural networks, deep learning-based watermarking methods have been widely studied. However, most existing methods which adopt simple embedding and extraction structures cannot fully utilize the image features. In this paper, we propose a novel Single-Encoder-Dual-Decoder (SEDD) watermarking architecture to achieve high imperceptibility and strong robustness. Precisely, the single encoder utilizes normalizing flow to realize watermark embedding, which can effectively fuse the watermark and cover image. For watermark extraction, we introduce a parallel dual-decoder to improve the imperceptibility and extracting ability. Extensive experiments demonstrate that better watermark robustness and imperceptibility are obtained by SEDD architecture. Our method achieves a bit error rate less than 0.1% under most attacks such as JPEG compression, Gaussian blur and crop. Besides, the proposed method also obtains strong robustness under combined attacks and social platform processing.

In moment-based watermarking schemes, the accuracy of the moments is crucial for constructing robust watermarking schemes. The robustness of the watermarking scheme relies heavily on the proper representation of the moments. Despite the importance, current theoretical research on accuracy is very limited in watermarking techniques. To this end, we propose a novel robust image watermarking scheme based on accurate polar harmonic Fourier moments (PHFMs). Specifically, the accurate PHFMs computation based on polar pixel tiling with nearest neighbor interpolation (PPTN) is designed. This computation is general and used for embedder and extractor. This ingenious design eliminates geometric and numerical integration errors and also avoids the distortion interaction caused by watermarks. Also, an improved quantization strategy is applied to the embedding process, and satisfactory imperceptibility is obtained. The watermark is extracted without the host image. The experimental results show the excellent robustness of the proposed watermarking scheme to common image processing attacks, geometric attacks, and some kinds of compound attacks. The proposed scheme is superior to the state-of-the-art image watermarking schemes.

In recent years, online video sharing platforms have been widely available on social networks. To protect copyright and track the origins of these shared videos, some video watermarking methods have been proposed. However, their robustness performance is significantly degraded under geometric deformations, which destroy the synchronization between the watermark embedding and extraction. To this end, we propose a novel robust blind video watermarking scheme by embedding the watermark into low-order recursive Zernike moments. To reduce the time complexity, we give an efficient computation method by exploring the characteristics of video and moments. The moment accuracy is greatly improved due to the introduction of a recursive computation method. Furthermore, we design an optimization strategy to enhance visual quality and reduce distortion drift of watermarked videos by analyzing the radial basis function. The robustness of the proposed scheme is verified by different attacks, including geometric deformations, length-width ratio changes, temporal synchronization attacks, and combined attacks. In practical applications, the proposed scheme effectively resists processing from video sharing platforms and screenshots taken with smartphones and PC monitors. The watermark is extracted without the host video. Experimental results show that our proposed scheme outperforms other state-of-the-art schemes in terms of imperceptibility and robustness.